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Published on: December 11, 2016
Artificial neural network-based pharmacogenomic algorithm for warfarin dose optimization
Addepalli Pavani1, Shaik Mohammad Naushad2, Rajasekar Manoj Kumar2
1Department of Clinical Pharmacology & Therapeutics, Nizam's Institute of Medical Sciences, Hyderabad 500082, India.
Aim:
To develop more precise pharmacogenomic algorithm for prediction of safe and effective dose of warfarin.
Materials & Methods:
An artificial neural network (ANN) algorithm was developed by using age, gender, BMI, plasma vitamin K levels, thyroid status and ten genetic variables as the inputs and therapeutic warfarin dose as the output. Hyperbolic tangent function was used to build an ANN architecture.
Results:
This model explained 93.5% variability in warfarin dosing and predicted warfarin dose accurately in 74.5% patients whose international normalized ratio (INR) was less than 2.0 and in 83.3% patients whose INR was more than 3.5. This algorithm reduced the out-of-range INRs (odds ratio [OR]: 0.49; 95% CI: 0.30-0.79; p = 0.003), the rate of adverse drug reactions (OR: 0.00; 95% CI: 0.00-1.21; p = 0.06) and time to reach first therapeutic INR (OR: 6.73; 95% CI: 2.17-22.31; p < 0.0001). This algorithm was found to be applicable in both euthyroid and hypothyroid status. S-warfarin/7-hydroxywarfarin ratio was found to increase in subjects with CYP2C9*2 and CYP2C9*3 justifying the warfarin sensitivity attributed to these variants.
Conclusion:
An application of ANN for warfarin dosing improves predictability and provides safe and effective dosing.
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